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The Coherent Backscattering (CBS) peak is a well-known interferential signature of weak localization in disordered or chaotic systems. Recently, it was realized that another interferential peak, the Coherent Forward Scattering (CFS) peak, emerges in the presence of strong localization. This peak has never been observed directly to date. We report the first direct observation of the CFS peak and demonstrate its dual role as signature of non-ergodicity and as probe of symmetries in quantum chaotic systems. Using a shaken rotor model realized with a Bose-Einstein condensate (BEC) of ultracold atoms in a modulated optical lattice, we investigate dynamical localization in momentum space. The CFS peak emerges in the position distribution as a consequence of non-ergodic dynamics, while its growth timescale depends critically on the localization scale. By finely tuning the modulation, we control the symmetries of the dynamics (time-reversal and parity) and measure their impact on both CFS and CBS peaks. Our results highlight the strong link of CFS and its temporal growth with symmetry and localization properties, establishing CFS as a robust quantitative marker of non-ergodicity. This work opens new avenues for characterizing non-ergodicity and symmetries in quantum chaotic or disordered systems, with possible applications in many-body localization and many-body chaos., Comment: 15 pages, 12 figures

Food security is a complex, multidimensional concept challenging to measure comprehensively. Effective anticipation, monitoring, and mitigation of food crises require timely and comprehensive global data. This paper introduces the Harmonized Food Insecurity Dataset (HFID), an open-source resource consolidating four key data sources: the Integrated Food Security Phase Classification (IPC)/Cadre Harmonis\'e (CH) phases, the Famine Early Warning Systems Network (FEWS NET) IPC-compatible phases, and the World Food Program's (WFP) Food Consumption Score (FCS) and reduced Coping Strategy Index (rCSI). Updated monthly and using a common reference system for administrative units, the HFID offers extensive spatial and temporal coverage. It serves as a vital tool for food security experts and humanitarian agencies, providing a unified resource for analyzing food security conditions and highlighting global data disparities. The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises., Comment: The authors Melissande Machefer and Michele Ronco have contributed equally as both first authors to this work. This work is currently being reviewed in a peer-reviewed journal

Photonic quantum information processing in metropolitan quantum networks lays the foundation for cloud quantum computing [1, 2], secure communication [3, 4], and the realization of a global quantum internet [5, 6]. This paradigm shift requires on-demand and high-rate generation of flying qubits and their quantum state teleportation over long distances [7]. Despite the last decade has witnessed an impressive progress in the performances of deterministic photon sources [8-11], the exploitation of distinct quantum emitters to implement all-photonic quantum teleportation among distant parties has remained elusive. Here, we overcome this challenge by using dissimilar quantum dots whose electronic and optical properties are engineered by light-matter interaction [12], multi-axial strain [13] and magnetic fields [14] so as to make them suitable for the teleportation of polarization qubits. This is demonstrated in a hybrid quantum network harnessing both fiber connections and 270 m free-space optical link connecting two buildings of the University campus in the center of Rome. The protocol exploits GPS-assisted synchronization, ultra-fast single photon detectors as well as stabilization systems that compensate for atmospheric turbulence. The achieved teleportation state fidelity reaches up to 82+-1%, above the classical limit by more than 10 standard deviations. Our field demonstration of all-photonic quantum teleportation opens a new route to implement solid-state based quantum relays and builds the foundation for practical quantum networks.

We establish a structure theorem, analogous to the classical result of Milnor and Moore, for differential graded Hopf algebras: any differential Hopf algebra $H$ that is free as a coalgebra carries an underlying $B_\infty$ algebra structure that restricts to the subspace of primitives, and conversely $H$ may be recovered via a universal enveloping differential-2-associative algebra. This extends the work of Loday and Ronco [12] where the ungraded non-differential case was treated, and only the multibrace part of the $B_\infty$ structure was found. We show that the multibrace structure of [12] originates from a twisting of a quasi-trivial structure, extending the work of Markl [14] on the $A_\infty$ structure underlying any algebra with a square-zero endomorphism. In this framework it is also clear that the multibrace and $A_\infty$ structures are compatible, and provide an appropriate $B_\infty$ structure for the structure theorem., Comment: 18pp

Smart sensors are an emerging technology that allows combining the data acquisition with the elaboration directly on the Edge device, very close to the sensors. To push this concept to the extreme, technology companies are proposing a new generation of sensors allowing to move the intelligence from the edge host device, typically a microcontroller, directly to the ultra-low-power sensor itself, in order to further reduce the miniaturization, cost and energy efficiency. This paper evaluates the capabilities of a novel and promising solution from STMicroelectronics. The presence of a floating point unit and an accelerator for binary neural networks provide capabilities for in-sensor feature extraction and machine learning. We propose a comparison of full-precision and binary neural networks for activity recognition with accelerometer data generated by the sensor itself. Experimental results have demonstrated that the sensor can achieve an inference performance of 10.7 cycles/MAC, comparable to a Cortex-M4-based microcontroller, with full-precision networks, and up to 1.5 cycles/MAC with large binary models for low latency inference, with an average energy consumption of only 90 $\mu$J/inference with the core running at 5 MHz.

The radius valley separating super-Earths from mini-Neptunes is a fundamental benchmark for theories of planet formation and evolution. Observations show that the location of the radius valley decreases with decreasing stellar mass and with increasing orbital period. Here, we build from our previous pebble-based formation model, which, combined with photoevaporation after disc dispersal, unveiled the radius valley as a separator between rocky- and water-worlds. We expand our models for a range of stellar masses spanning from 0.1 to 1.5 $M_\odot$. We find that the location of the radius valley is well described by a power-law in stellar mass as $R_{\rm valley} = 1.8197 \, M_{\star}^{\!0.14({+0.02}/{-0.01})}$, which is in excellent agreement with observations. We also find very good agreement with the dependence of the radius valley on orbital period, both for FGK- and M-dwarfs. Additionally, we note that the radius valley gets filled towards low stellar masses, particularly at 0.1-0.4 $M_\odot$, yielding a rather flat slope in $R_{\rm valley} - P_{\rm orb}$. This is the result of orbital migration occurring at lower planet mass for less massive stars, which allows for low-mass water-worlds to reach the inner regions of the system, blurring the separation in mass (and size) between rocky- and water-worlds. Furthermore, we find that for planetary equilibrium temperatures above 400 K, the water in the volatile layer exists fully in the form of steam, puffing the planet radius up compared to condensed-water worlds. This produces an increase in planet radii of $\sim 30\%$ at 1 $M_\oplus$, and of $\sim 15\%$ at 5 $M_\oplus$, compared to condensed-water-worlds. As with Sun-like stars, pebble accretion leaves its imprint on the overall exoplanet population as a depletion of planets with intermediate compositions, carving a valley in planet density for all spectral types (abridged)., Comment: Accepted for publication in A&A Letters. Results from simulations available at https://zenodo.org/records/10719523

For any set S, the free magmatic algebra spanned by card(S) binary products is the vector space spanned by the set of all planar rooted binary trees with the internal nodes colored by the elements of S, graded by the number of leaves of a tree. We show that it has a unique structure of coassociative coalgebra such that the coproduct satisfies the unital infinitesimal condition with each magmatic product, and prove an analog of Aguiar-Sottile formula in this context, describing the coproduct in terms of the Moebius basis for the Tamari order. The last result allows us to compute the subspace of primitive elements of any unital infinitesimal S-magmatic bialgebra. As an example, we construct a set of generators of the dual of Pilaud and Pons bialgebra of integer relations and compute an explicit basis of its subspace of primitive elements., Comment: 36 pages and 15 figures

The study of protoplanetary disc evolution and planet formation has mainly concentrated on solar (and low) mass stars since they host the majority of the confirmed exoplanets. Nevertheless, the numerous planets found orbiting stars up to $\sim3M_\odot$ has sparked interest in understanding how they form and how their hosting discs evolve. Our goal is to improve our knowledge on the gas disc evolution around intermediate mass stars for future planet formation studies. We study the long-term evolution of protoplanetary discs affected by viscous accretion, X-ray and FUV photoevaporation from the central star around stars between $1 - 3M_\odot$ considering the effects of stellar evolution. We explore different values of the viscosity parameter and the initial mass of the disc. We find that the evolutionary pathway of disc dispersal depends on the stellar mass. Our simulations reveal four distinct evolutionary pathways for the gas disc not reported before that are a consequence of stellar evolution, and which will likely impact dust evolution and planet formation. As the stellar mass grows from 1 to $\sim2M_\odot$, the disc evolution changes from the conventional inside-out clearing to a homogeneous disc evolution scenario where both inner and outer discs, formed after photoevaporation opened a gap, vanish over a similar timescale. As the stellar mass continues to increase, reaching $\sim 3M_\odot$, we have identified a distinct pathway that we refer to as revenant disc evolution, where the inner and outer discs reconnect after the gap opened. For the largest masses, we observe outside-in disc dispersal, in which the outer disc dissipates first due to the strong FUV photoevaporation. Revenant disc evolution stands out as it is capable of extending the disc lifespan. Otherwise, the disc dispersal time scale decreases with increasing stellar mass except for low viscosity discs., Comment: 20 pages, 13 figures. Resubmitted to A&A after minor revisions

Range-measuring sensors play a critical role in autonomous driving systems. While LiDAR technology has been dominant, its vulnerability to adverse weather conditions is well-documented. This paper focuses on secondary adverse conditions and the implications of ill-reflective surfaces on range measurement sensors. We assess the influence of this condition on the three primary ranging modalities used in autonomous mobile robotics: LiDAR, RADAR, and Depth-Camera. Based on accurate experimental evaluation the papers findings reveal that under ill-reflectivity, LiDAR ranging performance drops significantly to 33% of its nominal operating conditions, whereas RADAR and Depth-Cameras maintain up to 100% of their nominal distance ranging capabilities. Additionally, we demonstrate on a 1:10 scaled autonomous racecar how ill-reflectivity adversely impacts downstream robotics tasks, highlighting the necessity for robust range sensing in autonomous driving., Comment: Accepted at IEEE 9th World Forum on Internet of Things

Non-contact vital sign monitoring has many advantages over conventional methods in being comfortable, unobtrusive and without any risk of spreading infection. The use of millimeter-wave (mmWave) radars is one of the most promising approaches that enable contact-less monitoring of vital signs. Novel low-power implementations of this technology promise to enable vital sign sensing in embedded, battery-operated devices. The nature of these new low-power sensors exacerbates the challenges of accurate and robust vital sign monitoring and especially the problem of heart-rate tracking. This work focuses on the investigation and characterization of three Frequency Modulated Continuous Wave (FMCW) low-power radars with different carrier frequencies of 24 GHz, 60 GHz and 120 GHz. The evaluation platforms were first tested on phantom models that emulated human bodies to accurately evaluate the baseline noise, error in range estimation, and error in displacement estimation. Additionally, the systems were also used to collect data from three human subjects to gauge the feasibility of identifying heartbeat peaks and breathing peaks with simple and lightweight algorithms that could potentially run in low-power embedded processors. The investigation revealed that the 24 GHz radar has the highest baseline noise level, 0.04mm at 0{\deg} angle of incidence, and an error in range estimation of 3.45 +- 1.88 cm at a distance of 60 cm. At the same distance, the 60 GHz and the 120 GHz radar system shows the least noise level, 0.0lmm at 0{\deg} angle of incidence, and error in range estimation 0.64 +- 0.01 cm and 0.04 +- 0.0 cm respectively. Additionally, tests on humans showed that all three radar systems were able to identify heart and breathing activity but the 120 GHz radar system outperformed the other two.

The recent ubiquitous adoption of remote conferencing has been accompanied by omnipresent frustration with distorted or otherwise unclear voice communication. Audio enhancement can compensate for low-quality input signals from, for example, small true wireless earbuds, by applying noise suppression techniques. Such processing relies on voice activity detection (VAD) with low latency and the added capability of discriminating the wearer's voice from others - a task of significant computational complexity. The tight energy budget of devices as small as modern earphones, however, requires any system attempting to tackle this problem to do so with minimal power and processing overhead, while not relying on speaker-specific voice samples and training due to usability concerns. This paper presents the design and implementation of a custom research platform for low-power wireless earbuds based on novel, commercial, MEMS bone-conduction microphones. Such microphones can record the wearer's speech with much greater isolation, enabling personalized voice activity detection and further audio enhancement applications. Furthermore, the paper accurately evaluates a proposed low-power personalized speech detection algorithm based on bone conduction data and a recurrent neural network running on the implemented research platform. This algorithm is compared to an approach based on traditional microphone input. The performance of the bone conduction system, achieving detection of speech within 12.8ms at an accuracy of 95\% is evaluated. Different SoC choices are contrasted, with the final implementation based on the cutting-edge Ambiq Apollo 4 Blue SoC achieving 2.64mW average power consumption at 14uJ per inference, reaching 43h of battery life on a miniature 32mAh li-ion cell and without duty cycling.

This paper presents the design and development of an intelligent subsystem that includes a novel low-power radar sensor integrated into an autonomous racing perception pipeline to robustly estimate the position and velocity of dynamic obstacles. The proposed system, based on the Infineon BGT60TR13D radar, is evaluated in a real-world scenario with scaled race cars. The paper explores the benefits and limitations of using such a sensor subsystem and draws conclusions based on field-collected data. The results demonstrate a tracking error up to 0.21 +- 0.29 m in distance estimation and 0.39 +- 0.19 m/s in velocity estimation, despite the power consumption in the range of 10s of milliwatts. The presented system provides complementary information to other sensors such as LiDAR and camera, and can be used in a wide range of applications beyond autonomous racing.

Gaze estimation is a valuable technology with numerous applications in fields such as human-computer interaction, virtual reality, and medicine. This report presents the implementation of a gaze estimation system using the Sony Spresense microcontroller board and explores its performance in latency, MAC/cycle, and power consumption. The report also provides insights into the system's architecture, including the gaze estimation model used. Additionally, a demonstration of the system is presented, showcasing its functionality and performance. Our lightweight model TinyTrackerS is a mere 169Kb in size, using 85.8k parameters and runs on the Spresense platform at 3 FPS.

We present polarized |S|=0.99$\pm$0.01, and unpolarized |S|=0.03$\pm$0.01 emission from a single emitter embedded in a single, cylindrically symmetric device design. We show that the polarization stems from a position offset of the single emitter with respect to the cavity center, which breaks the cylindrical symmetry, and a position-dependent coupling to the frequency degenerate eigenmodes of the resonator structure. The experimental results are interpreted by using numerical simulations and by experimental mapping of the polarization-resolved far-field emission patterns. Our findings can be generalized to any nanophotonic structure where two orthogonal eigenmodes are not fully spatially overlapping., Comment: 15 pages, 5 figures

The discovery of quantum emitters (QEs) in two-dimensional materials (2D) has triggered a surge of research to assess their suitability for quantum photonics. While their microscopic origin is still the subject of intense studies, position-controlled QEs are routinely fabricated using static strain gradients, which are used to drive excitons towards localized regions of the crystal where quantum light emission takes place. However, the use of strain in a dynamic fashion to control the brightness of single-photon sources in 2D materials has not been explored so far. In this work, we address this challenge by introducing a novel hybrid semiconductorpiezoelectric device in which WSe2 monolayers are integrated onto piezoelectric pillars that provide both static and dynamic strains. The static strains are first used to induce the formation of QEs, whose emission shows photon anti-bunching. Their energy and brightness are then controlled via the application of voltages to the piezoelectric pillars. Numerical simulations combined with drift-diffusion equations show that these effects are due to a strain-induced modification of the confining-potential landscape, which in turn leads to a net redistribution of excitons among the different QEs. Our work provides a method to dynamically control the brightness of single photon sources based on 2D materials., Comment: 22 pages, 5 figures

The goal of this study is to assess the feasibility of airway geometry as a biomarker for ASD. Chest CT images of children with a documented diagnosis of ASD as well as healthy controls were identified retrospectively. 54 scans were obtained for analysis, including 31 ASD cases and 23 age and sex-matched controls. A feature selection and classification procedure using principal component analysis (PCA) and support vector machine (SVM) achieved a peak cross validation accuracy of nearly 89% using a feature set of 8 airway branching angles. Sensitivity was 94%, but specificity was only 78%. The results suggest a measurable difference in airway branchpoint angles between children with ASD and the control population. Under review at Scientific Reports

Two-photon resonant excitation of the biexciton-exciton cascade in a quantum dot generates highly polarization-entangled photon pairs in a near-deterministic way. However, the ultimate level of achievable entanglement is still debated. Here, we observe the impact of the laser-induced AC-Stark effect on the quantum dot emission spectra and on entanglement. For increasing pulse-duration/lifetime ratios and pump powers, decreasing values of concurrence are recorded. Nonetheless, additional contributions are still required to fully account for the observed below-unity concurrence., Comment: The first two authors contributed equally to this work. 8 pages, 4 figures

Entanglement-based quantum key distribution can enable secure communication in trusted node-free networks and over long distances. Although implementations exist both in fiber and in free space, the latter approach is often considered challenging due to environmental factors. Here, we implement a quantum communication protocol during daytime for the first time using a quantum dot source. This technology presents advantages in terms of narrower spectral bandwidth -- beneficial for filtering out sunlight -- and negligible multiphoton emission at peak brightness. We demonstrate continuous operation over the course of three and a half days, across an urban 270-m-long free-space optical link, under different light and weather conditions., Comment: The first two authors contributed equally to this work. 10 pages, 3 figures

The combined increase of energy demand and environmental pollution at a global scale is entailing a rethinking of the production models in sustainable terms. As a consequence, energy suppliers are starting to adopt strategies that flatten demand peaks in power plants by means of pricing policies that stimulate a change in the consumption practices of customers. A representative example is the Time-of-Use (TOU)-based tariffs policy, which encourages electricity usage at off-peak hours by means of low prices, while penalizing peak hours with higher prices. The TOU-based tariffs policy induces a partitioning of the time horizon into a set of time slots, each associated with a cost that becomes a part of the optimization objective. This thesis focuses on a representative bi-objective energy-efficient job scheduling problem on parallel identical machines under TOU-based tariffs by delving into the description of its inherent properties, mathematical formulations, and solution approaches. Specifically, the thesis starts by reviewing the flourishing literature on the subject, and providing a useful framework for theoreticians and practitioners. Subsequently, it describes the considered problem and investigates its theoretical properties. In the same chapter, it presents a first mathematical model for the problem, as well as a possible reformulation that exploits the structure of the solution space so as to achieve a considerable increase in compactness. Afterwards, the thesis introduces a sophisticated heuristic scheme to tackle the inherent hardness of the problem, and an exact algorithm that exploits the mathematical models. Then, it shows the computational efficiency of the presented solution approaches on a wide test benchmark. Finally, it presents a perspective on future research directions for the class of energy-efficient scheduling problems under TOU-based tariffs as a whole.

Gamma-ray astronomy has become one of the main experimental ways to test the modified dispersion relations (MDRs) of photons in vacuum, obtained in some attempts to formulate a theory of Quantum Gravity. The MDRs in use imply time delays which depend on the energy, and which increase with distance following some function of redshift. The use of transient, or variable, distant and highly energetic sources, already allows us to set stringent limits on the energy scale related to this phenomenon, usually thought to be of the order of the Planck energy, but robust conclusions on the existence of MDR-related propagation effects still require the analysis of a large population of sources. In order to gather the biggest sample of sources possible for MDR searches at teraelectronvolt energies, the H.E.S.S., MAGIC and VERITAS collaborations enacted a joint task force to combine all their relevant data to constrain the Quantum Gravity energy scale. In the present article, the likelihood method used, to combine the data and provide a common limit, is described in detail and tested through simulations of recorded data sets for a gamma-ray burst, three flaring active galactic nuclei and two pulsars. Statistical and systematic errors are assessed and included in the likelihood as nuisance parameters. In addition, a comparison of two different formalisms for distance dependence of the time lags is performed for the first time. In a second article, to appear later, the method will be applied on all relevant data from the three experiments., Comment: 19 pages, 9 figures, submitted to ApJ. Latest version including first referee's comments

Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, using historical Earth observation data from the last decade to predict next-day's fire danger. To that end, we collect, pre-process and harmonize an open-access datacube, featuring a set of covariates that jointly affect the fire occurrence and spread, such as weather conditions, satellite-derived products, topography features and variables related to human activity. We implement a variety of Deep Learning (DL) models to capture the spatial, temporal or spatio-temporal context and compare them against a Random Forest (RF) baseline. We find that either spatial or temporal context is enough to surpass the RF, while a ConvLSTM that exploits the spatio-temporal context performs best with a test Area Under the Receiver Operating Characteristic of 0.926. Our DL-based proof-of-concept provides national-scale daily fire danger maps at a much higher spatial resolution than existing operational solutions., Comment: Accepted to the workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)